ML platform

Get to insights faster and achieve greater business impact. Efficiently scale machine learning efforts in the enterprise while adopting MLOps and increasing the quality of insights. Deploy in the cloud, in the datacenter, or at the edge.

Productivity and Scalability, ML Platform - Grid Dynamics

Boost the performance of the data science team

Data scientists’ time is expensive. Don’t waste it on non-differentiated work and avoid reinventing the wheel by creating machine learning platforms from scratch. A good ML platform will support the machine learning lifecycle from data ingestion to model serving and monitoring, increase the productivity of data analysts by 10x, support machine learning software and frameworks, enable automated machine learning, and let you scale the team more efficiently.

Increase the quality of ML decisions

The cost of errors in machine learning is getting higher as companies increasingly rely on closed-loop systems. Implementing model testing, data quality, model monitoring, and anomaly detection decreases the chances of production issues and facilitates high-quality insights.

MLOps, ML Platform - Grid Dynamics

Consistently deliver actionable insights

DevOps and Continuous Delivery became standard in application development long ago. But the core principles of DevOps can be expanded to the machine learning process within your business. With the right platform you can further increase efficiency with automated machine learning and by providing necessary machine learning algorithms and frameworks including deep learning and automl.

Deploy in the cloud

Using the cloud to enable new machine learning use cases is the simplest way to begin the cloud journey for data analytics. Migrate or deploy a new cloud platform to increase the agility and productivity of the data science team. Use it as a prototype for the larger cloud migration and let the data gravity shift to the cloud over time.

Make data-driven decisions at the edge

Some companies have significant infrastructure at the edge. Factories, stores, branches, distribution centers, gas stations, and a variety of IoT use cases may take advantage of deploying machine learning models locally to lower latency and make decisions without internet connectivity. These companies can take advantage of open source-based infrastructure agnostic data science platforms to make decisions in real-time at the edge.

A machine learning platform should support the end-to-end data science and machine learning lifecycle, facilitate collaboration between data analysts and data scientists, and enable the MLOps process. The main capabilities of the AI platform should include data ingestion, data preparation, and data exploration. It should also include feature selection, feature engineering, prototyping, experimentation, model training, validation, model testing, deployment to production, model serving, and monitoring. A good platform should support a variety of machine learning algorithms including predictive analytics, deep learning, reinforcement learning, and the creation of various types of neural networks, etc. A data science and machine learning platform is typically an extension of an enterprise data analytics platform and should support a variety of integrations. There are a variety of product vendors offering software as a service solutions. All major cloud providers have their own data science platform offerings. Good open source-based options exist too. Different options may work best for different companies, depending on their machine learning use cases, the maturity of the team, whether they are in the datacenter or in the cloud, and what cloud provider they’ve selected. Our focus is on making the right choice for the right circumstances. We go beyond the deployment of the AI platform. We help you choose the right one, integrate it with the data lake or analytical data platform, make the data available, onboard the MLOps process, train data scientists, implement a common library of machine learning models, and ensure that the data science process works smoothly from data to insights. Choose ML Platform - Grid Dynamics

We have developed advanced artificial intelligence use cases, machine learning platforms, and onboard MLOps processes for Fortune-1000 enterprises across various industries including telecom, retail, media, gaming, and financial services.

We provide flexible engagement options to design and build ML platforms and artificial intelligence use cases, and onboard the MLOps process and culture. Contact us today to get started with a workshop, discovery, or PoC.


We offer free half-day workshops with our top experts in ML platforms and MLOps and real-time analytics to discuss your stream processing strategy, challenges, optimization opportunities, and industry best practices. 


If you have already identified a need to improve the machine learning process and onboard an ML platform, we can start with a 4–8-week proof-of-concept project to deliver tangible results for your enterprise.


If you’re at the requirements analysis stage, we can start with a 2–3-week discovery phase to identify the current challenges, perform gap analysis, design your solution, and build an implementation and training roadmap.